1. Field of the Invention
The present teachings generally relate to the field of solubility determination and, more particularly, to a system and method for predicting aqueous solubility.
2. Description of the Related Art
In the field of drug development, screening methods are often utilized to profile large collections of synthesized and virtual libraries for candidate compounds of pharmaceutical interest. During the screening process, the solubility of a selected compound may be assessed and represents an important consideration in terms of compound synthesis/processing, pharmacokinetic analysis, and evaluation of potential toxicity. Accurate solubility prediction is also useful in characterizing drug candidates and evaluating how functional group derivatives of the drug may be absorbed and distributed within a biological system.
Conventional methods for predicting solubility are typically not well suited for evaluating large numbers of compounds and may limit the speed and efficiency of drug discovery. A summary of selected conventional methodologies is discussed below. A more detailed review of conventional methods for predictive solubility determination can be found in Aqueous Solubility: Methods of Estimation for Organic Compounds; Yalkowsky and Banerjee; Dekker, New York (1992).
Some conventional methods for solubility prediction are based upon semi-empirical molecular orbital or quantum calculations. These approaches generally utilize complex 3-dimensional (3D) molecular descriptors to represent structural features with electronic properties such as atomic charges computed using molecular orbital calculations. A limitation encountered when using these types of methods is that they are generally directed towards the prediction of the solubility for small organic compounds and may not be suitable for solubility prediction for larger or more complex compounds such as drugs. Additionally, these approaches may involve solving quantum-based calculations that are computationally-intensive tasks that may not be suitable for rapid analysis of large numbers of molecules. Other conventional approaches require consideration of numerous molecular properties when performing the analysis to reproduce experimentally determined solubilities and may be limited to development of solubility models only when there is sufficient previously identified physiochemical property information.
Other conventional approaches to solubility prediction incorporate neural network modeling and Monte Carlo based simulation approaches. Typically, conventional models utilize numerous adjustable weighted variables and parameters that may lead to undesirably complex equations thereby reducing the speed with which solubility analysis may be performed. Like other conventional methods, Monte Carlo based modeling approaches may require 3D molecular structure information thus limiting their utility in analysis of large data sets and mining of extensive collections of compounds.
From the foregoing it will be appreciated that there is a need for an improved method for predictive solubility determination. In one aspect, it is desirable for such a method to be reasonably accurate and capable of rapid analysis even for large data sets. Moreover, it is desirable for the analysis methodology to incorporate a reduced number of descriptors or parameters so as to facilitate analysis and improve computational performance.